You don't need a $100,000 MBA

Here are 9 valuable lessons that took me 5 years to learn.

( 3 minutes read )

1. Let's talk about the effects of #climatechange on the world's forests, with a focus on the northwest of Canada. This is also about electoral #greenwash, and my duty as public servant and right as private Canadian citizen to speak freely about what I know.

2. Why am I compelled to speak? How am I free to speak? The answer is in the federal Values and Ethics Code of Coduct (VECC).

3. It can be career-limiting to criticize government policy when you work for government. Duty of Loyalty is very, very important. However, as VECC makes clear, it is not absolute. In fact SCoC has ruled twice in favour of citizen free speech as a higher ideal. In 1985 and 2001.

4. I am therefore perfectly comfortable showing you the scary things that are happening in the North under a warming climate. A failure to meet global GHG emissions targets is hurting Canada's forests in a way we used to think wouldn't happen for decades.

5. Warming temperatures and rising [CO2] could, in theory, help green the planet with more vigorous tree growth. But that's not what's happening. Don't believe the #greenwash. The positive effects are over and it is warm enough that we are now starting the into the negative.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

Trump is a Russian asset, a mob money launderer installed by his mob whoremasters to sow chaos and weaken the US & our allies.

EVERY SINGLE THING HE’S DONE since taking office is to achieve those purposes—that and to enrich himself personally.

[THREAD]

1/ His entire family is part of this. Melania is a fucking crook. Ivanka is a fucking crook. Junior and Gums are fucking crooks. They will all die in prison, except for Kushner, who, if he really *did* trade US intel for money, should be executed for espionage.

2/ Why do you think Trump installs uniquely corrupt & awful people to Cabinet positions? Pruitt, Zinke, Betsy, Rex/Pompeo, Wilbur "Melting Nazi Face" Ross. They are not there to serve us. They are not there to drain the swamp. They are there to BURN THE REPUBLIC DOWN.

3/ Trump steals. He takes taxpayer money—your money and mine—and lines his pockets with it. He exploits his office to make himself richer. This is all he cares about. Why are Republican citizens, you “fiscal conservatives,” not more angry about this? Riddle me THAT, #MAGA.

4/ Matt Whitaker, the human dildo and “acting AG,” was illegally installed to throw a monkey wrench in the Mueller investigation. That’s why they won’t release his financials. He’s fucking bought and sold. He’s going to prison for obstruction.

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Great thread full of potential https://t.co/jA8h08M48z ideas! 💡

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